1. Field of the Invention
This application is based upon and claims the benefit of priority from Japanese patent application No. 2006-212660, filed on Aug. 3, 2006, the disclosure of which is incorporated herein in its entirety by reference.
The present invention relates to an apparatus and the like used for processing a digital image with many background noises, such as an image of a latent fingerprint, by using a computer. More specifically, the present invention relates to a line noise eliminating apparatus and the like, which can effectively eliminate the noise of straight-line form.
2. Description of the Related Art
In general, a fingerprint including a great number of ridgelines in streaked patterns has two outstanding features; one is that it is immutable throughout one's life, and the other is that nobody has the same fingerprint. Therefore, fingerprints have been used in criminal investigations from old times. In particular, collation using the latent fingerprints left behind in criminal scenes is effective as a way to help the investigations. Recently, many police forces have employed a fingerprint matching system that uses a computer, and conduct matching of the latent fingerprints.
However, many of the images of the latent fingerprints are of low quality with a noise, which makes it difficult for an investigator to make a judgment. This is also a large factor for hindering the system from being automated. There are many kinds of background noises in the latent fingerprints. One of those is a straight-form line. FIG. 6 illustrates an example of a latent fingerprint left on a check. As in the example shown in FIG. 6, there are cases where fingerprint ridgelines are left on the ruled lines of the check or on the background noise of a line pattern. With a related technique, such line noises are likely to be misjudged and extracted as the fingerprint ridgelines, so that it is difficult to enhance or extract only the fingerprint ridgelines.
As a related technique for eliminating the background pattern noise, it is common to employ Fourier transformation. Such technique is proposed in “Background Pattern Removal by Power Spectral Filtering” by Cannon, et al., Applied Optics, Mar. 15, 1983 (Non-patent Document 1), for example.
When this technique is employed for eliminating the line noises from a fingerprint image, it is necessary for the line noises to appear periodically. Thus, the effect thereof is limited. Further, when the periodicity of the line noises is similar to the periodicity of the fingerprint ridgelines, the fingerprint ridgelines are eliminated as well. Thus, the effect thereof is limited in that sense as well. Furthermore, the density of the fingerprint ridgelines in the area with no line noise is deteriorated with the line noise eliminating processing, so that the effect thereof is also limited.
FIG. 16B illustrates the state where the line noises are eliminated from the fingerprint image of FIG. 14A by the related technique. In the case where the periodicity of the line noises is insignificant as in the case of this fingerprint image, the eliminating performance is not sufficient.
FIG. 16A illustrates the state where the line noises are eliminated from the fingerprint image of FIG. 6 by the related technique. It can be seen from this example of fingerprint image that the density of the fingerprint ridgelines is also deteriorated.
Further, there are various measures proposed as a related method for enhancing the fingerprint ridgelines, in which the direction and periodicity of local ridge lines are extracted, and the ridgelines are enhanced through filter processing that corresponds to the extracted direction and periodicity. This method is proposed in “Fingerprint Image Enhancement: Algorithm and Performance Evaluation (1998)” by Hong, et al., IEEE Transactions on Pattern Analysis and Machine Intelligence (Non-patent Document 2) and Japanese Unexamined Patent Publication 2002-99912 (Patent Document 1).
However, such related technique is not effective when the direction and periodicity of the ridge lines cannot be extracted properly due to the influence of the line noise. Thus, the issue still remains to be overcome.
Furthermore, as a related technique for eliminating the line noise, Japanese Unexamined Patent Publication 2000-82110 (Patent Document 2) proposes a method for eliminating the ruled lines in particular. The method for detecting the ruled lines proposed therein calculates a black run towards a designated direction, and detects a peak of the histogram to recognize it as the ruled line.
However, when such related technique is employed for the fingerprint image, the fingerprint ridgelines may be mistakenly judged as the ruled lines. Thus, such method is not effective. The reason for this is that the straight-form fingerprint ridgelines and wide-width fingerprint ridgelines have long black runs.
Further, Japanese Unexamined Patent Publication 148-315135 (Patent Document 3) proposes a method as a related technique for detecting line segments in a drawing. However, this method detects the peak of an image histogram towards a designated direction to recognize the line segment.
When such related technique is employed for the fingerprint image, however, the fingerprint ridgelines may be mistakenly judged as the line segments. Thus, such method is not effective. The reason for this is that the straight-form fingerprint ridgelines and wide-width fingerprint ridgelines have a large image histogram.
Japanese Unexamined Patent Publication 2004-234333 (Patent Document 4) proposes a method as another related technique for detecting the line segment, in which edges are detected from an input image, and Hough transformation is applied to a binary image (the edges are binarized) so as to extract the line segment.
However, it is not possible to extract the line noise that crosses with the fingerprint ridge lines, even if this technique is employed for the latent fingerprint image with a conspicuous line noise. Thus, this method is not effective. The reason for this is that the edges of the line noises crossing with many fingerprint ridgelines become intermittent short line segments, so that the components after Hough transformation becomes insignificant.
Meanwhile, as a method for eliminating a local background noise, a local contrast stretch method (Adaptive Contrast Stretch) and a local histogram equalization method (Adaptive Histogram Equalization) are known to be effective. However, the enhancing effect cannot be expected with the local enhancing methods, unless the reference area is set properly.
For example, JP Patent Publication No. 3465226 (Patent Document 5) discloses an image density converting method capable of reducing the elimination of effective information through dividing an input image based on texture analysis, and determining the degree of smoothening the density histogram in accordance with the dimension of the dynamic range for each divided area.
However, even if the input image is divided into each area based on the texture analysis as proposed in Patent Document 5, it is difficult to accurately divide the area of the background noise such as the line noise. Therefore, the reference area covers over the background noise area as well as the non-background noise area in the vicinity of the border of the background noise. As a result, the reference area cannot be limited only to the background noise area, so that it is not possible to obtain the result of density conversion as it is expected. Further, the density converting method proposed in Patent Document 5 depends largely on the accuracy of area dividing method executed based on the texture analysis of the input image. Thus, the result of enhancement is deteriorated if the line noise area cannot be extracted properly.